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docs: remove deprecated Graph Memory references

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Graph Memory was deprecated on April 16, 2026. This removes remaining
mentions across docs that could mislead readers into thinking it is still
a supported feature.

- Platform pages (overview, quickstart, faqs, platform-vs-oss, vibecoding):
  remove graph memory from feature lists, comparison table, and intro copy
- Platform features (platform-overview, mcp-integration,
  advanced-memory-operations): rephrase "graph-powered retrieval" to
  "advanced retrieval"; drop "Enable graph memories" tip; remove graph-edge
  verification step from the metadata example
- Core concepts (add, search, memory-types, memory-evaluation): remove
  "optional graph storage" / "graph features" / "graph writes" / "graph
  toggles"; drop "graph" from Entity Search description; remove outdated
  add/search architecture diagrams (replacement pending)
- Open source features (overview, rest-api): remove "graph relationships"
  and "graph backend(s)" from REST server config narrative
- API reference (organizations-projects): drop "graph settings" from
  Update Project Settings description
- Cookbooks: remove "Graph Memory on Neptune" card (page removed) and
  Neptune mention from AWS Bedrock card; remove "Graph Capabilities"
  bullet from Gemini cookbook; remove graph-memory conditional bullet from
  controlling-memory-ingestion
- CLI: remove --graph / --no-graph flags from mem0 add / mem0 search
  flag tables and MEM0_ENABLE_GRAPH env var
- Images: delete orphan add_architecture.png and search_architecture.png
This commit is contained in:
rudrajmehta-mem0
2026-05-27 18:26:36 -07:00
parent 116c439b1d
commit 8296c06206
21 changed files with 21 additions and 46 deletions
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docs/platform/faqs.mdx
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@@ -9,7 +9,7 @@ iconType: "solid"
<Accordion title="How does Mem0 work?">
Mem0 utilizes a sophisticated hybrid database system to efficiently manage and retrieve memories for AI agents and assistants. Each memory is linked to a unique identifier, such as a user ID or agent ID, enabling Mem0 to organize and access memories tailored to specific individuals or contexts.
When a message is added to Mem0 via the `add` method, the system extracts pertinent facts and preferences, distributing them across various data stores: a vector database and a graph database. This hybrid strategy ensures that diverse types of information are stored optimally, facilitating swift and effective searches.
When a message is added to Mem0 via the `add` method, the system extracts pertinent facts and preferences, distributing them in a managed vector store. This strategy ensures that diverse types of information are stored optimally, facilitating swift and effective searches.
When an AI agent or LLM needs to access memories, it employs the `search` method. Mem0 conducts a comprehensive search across these data stores, retrieving relevant information from each.